[1]} argue that FlowQA is not quite natural since it does not mimic how humans perform reasoning.
Using IF layers, FlowQA first processes sequentially in passage, in parallel of question turns and then processes sequentially in question turns, in parallel of passage words. However, humans typically do not first perform reasoning in parallel for each question, and then refine the reasoning results across different turns. Doing so is sub-optimal because the results of previous reasoning processes are not incorporated into the current reasoning process. Therefore, [1]} propose an improved FLOW-based CMC reader, GraphFLOW, that captures dialog flow using a graph neural network. GraphFlow dynamically constructs a question-specific context graph from passage text at each turn, then models the temporal dependencies in the sequence of context graphs.
